Multiple Residual Learning Network for Single Image Super-Resolution
Renhe Liu, Sumei Li, Chunping Hou, Guoqing Lei · 2018
Deep residual convolutional neural network (CNN) has recently achieved great success in image super-resolution (SR). Because residual learning accelerates convergence rate and eases the difficulty for reconstructing high-resolution (HR) image, these CNN models can achieve higher peak signal to noise ratio (PSNR) values with lower training cost. However, residual image used in present residual network still contains much high frequency information, which increases learning burden and limits learning ability of residual network. Moreover, training a very deep network faces many obstacles and costs too much time. In this paper, we propose a multiple residual learning network (MRLN), which not only further simplifies information complexity of residual image and improves the accuracy of residual network, but also obviously reduces time cost for training a very deep CNN. In MRLN, we use a shallow network formed by 30-layer convolutional layers as basic model and train it for multiple times. The output of previous basic model is used as the HR input of the next one. In this way, an extremely large CNN is converted into a series connection of shallow networks. Fig. 1 shows PSNR of recent state-of-the-art CNN models for scale factor 2 on Set5, our method performs better than other methods and set a new level for SR.